Automated Model-Based Clinical Summarization of Key Patient Data
SHARPC Leaders: Dean Sittig, Adam Wright
Most clinical information systems are organized around data types (e.g. lab results, medications, problems, vital signs and notes); however, this organization does not usually match the cognitive patterns of clinicians, which tend to be problem-oriented and cut across data types. Project 3 is dedicated to developing methodologies for modeling and summarizing complex chronically-ill patients’ electronic health records, which will be enhanced with context-appropriate, evidence-based recommendations that improve clinician decision-making under information overload and time pressure. Creating such summaries is challenging, and depends on both a deep understanding of clinician cognitive processes and accurate models of clinical knowledge and practice. We will use the Rapid Assessment Process (RAP), a modification of traditional ethnography, to understand clinician summarization needs and to develop clinical requirements . After this portion of the project is well-underway, we will design automated methods of creating accurate, succinct, condition-dependent and independent computer-generated summaries of complex, chronically-ill patients with the ultimate goal of improving patient safety, clinician efficiency and satisfaction, and reduce the cost of care.
MAPLE Knowledge Base: A validated knowledge base that can be used to infer problems from medications, laboratory results, billing data, procedures and vital signs. The knowledge base is available at http://jamia.bmj.com/content/18/6/859/suppl/DC1 and is described in a paper cited below.
Problem-Medication Linkage Knowledge Base: An ontology-based knowledge base containing nearly 34 million distinct problem-medication pairs by:
1) using the “may_treat” relationship within NDF-RT, mapping the medications and problems to RxNorm and SNOMED-CT, and
2) inferring additional relationships using the “ingredient_of” and “isa” relationships between similar medications in RxNorm and derivative problems in SNOMED-CT.
An early version of this work with 7 million problem-medication pairs was described in an AMIA proceedings paper, cited below, and a substantially revised version is listed below as: A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record. The full KB (34 million pairs) is included as an Appendix in this manuscript.
- MedEx: Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes.
The code for this project can be found at: http://code.google.com/p/medex-uima
Demonstration of Patient Summarizer within the SMART App Platform
- D’Amore J. The Promise of the Continuity of Care Document. Master’s Thesis School of Biomedical Informatics, University of Texas Health Science Center at Houston. 2011.
- D’Amore JD, Sittig DF, Ness RB. How the Continuity of Care Document Can Advance Medical Research and Public Health. American Journal of Public Health 2012 Mar 15. PMID: 22420795.
- D’Amore JD, Sittig DF, Wright A, Iyengar MS, Ness RB. The Promise of the CCD: Challenges and Opportunity for Quality Improvement and Population Health. AMIA Fall Symposium, 2011:285-94. PMID: 22195080.
- Feblowitz JC, Wright A, Singh H, Samal L, Sittig DF. Summarization of clinical information: A conceptual model. J Biomed Inform. 2011 Aug;44(4):688-99. PMID: 21440086.
- Klann JG, McCoy AB, Wright A, Wattanasin N, Sittig DF, Murphy SN. Health care transformation through collaboration on open-source informatics projects: integrating a medical applications platform, research data repository, and patient summarization. Interact J Med Res. 2013 May 30;2(1):e11. doi: 10.2196/ijmr.2454.
- Laxmisan A, McCoy AB, Wright A, Sittig DF. Clinical Summarization Capabilities of Commercially-available and Internally-developed Electronic Health Records. Applied Clinical Informatics 2012; 3: 80–93. doi:10.4338/ACI-2011-11-RA-0066.
- McCoy AB, Melton GB, Wright A, Sittig DF. Clinical Decision Support for Colon and Rectal Surgery: An Overview. Clin Colon Rectal Surg. 2013 Mar;26(1):23-30.
- McCoy AB, Sittig DB, Wright A, Comparison of clinical knowledge bases for summarization of electronic health records. Stud Health Technol Inform. 2013;192:1217.
- McCoy AB, Wright A, Eysenbach G, Malin BA, Patterson ES, Xu H, Sittig DF. State of the art in clinical informatics: evidence and examples. Yearb Med Inform. 2013;8(1):13-9.
- McCoy AB, Wright A, Kahn MG, Shapiro J, Bernstam EV, Sittig DF. Matching Identifiers in Electronic Health Records: Implications for Duplicate Records and Patient Safety. BMJ Healthcare Quality & Safety. 2013;22:219-224 doi:10.1136/bmjqs-2012-001419.
- McCoy AB, Wright A, Laxmisan A, Ottosen MJ, McCoy JA, Butten D, Sittig DF. Development and evaluation of a crowdsourcing methodology for knowledge base construction: identifying relationships between clinical problems and medications. J Am Med Inform Assoc. 2012 Sep 1;19(5):713-8. Epub 2012 May 12.
- McCoy AB, Wright A, Laxmisan A, Singh H, Sittig DF. A Prototype Knowledge Base and SMART App to Facilitate Organization of Patient Medications by Clinical Problems. AMIA Fall Symposium, 2011:888-94. PMID: 22195147.
- McCoy AB, Wright A, Rogith D, Fathiamini S, Ottenbacher AJ, Sittig DF. Development of a clinician reputation metric to identify appropriate problem-medication pairs in a crowdsourced knowledge base.J Biomed Inform. 2013 Dec 7. pii: S1532-0464(13)00191-3. doi: 10.1016/j.jbi.2013.11.010.
- Murphy DR, Reis B, Kadiyala H, Hirani K, Sittig DF, Khan MM, Singh H. Electronic health record-based messages to primary care providers: valuable information or just noise? Arch Intern Med. 2012 Feb 13;172(3):283-5. PMID: 22332167.
- Murphy DR, Reis B, Sittig DF, Singh H. Notifications received by primary care practitioners in electronic health records: a taxonomy and time analysis. Am J Med. 2012 Feb;125(2):209.e1-7. PMID: 22269625.
- Osheroff JA, Teich JM, Levick D, Saldana L, Velasco FT, Sittig DF, Rogers KM, Jenders RA. Improving Outcomes with Clinical Decision Support: An Implementer’s Guide. Second Edition Healthcare Information and Management Systems Society, 2012.
- Radecki RP, Sittig DF. Application of Electronic Health Records to the 2011 Joint Commission’s National Patient Safety Goals. Journal of the American Medical Association 2011 Jul 6;306(1):92-3. PMID: 21730246.
- Singh H, Classen DC, Sittig DF. Creating an Oversight Infrastructure for Electronic Health Record-Related Patient Safety Hazards. J Patient Saf. 2011 Dec;7(4):169-174. PMID: 22080284.
- Sittig DB, Ash JS. On the Importance of Using a Multidimensional Sociotechnical Model to Study Health Information Technology. Ann Fram Med. 2011 Sep-Oct;9(5):390-1. PMID: 21911756.
- Sittig DF, Singh H. Defining Health Information Technology-related Errors: New Developments Since to Err is Human. Archives of Internal Medicine 171(14): 1279-1282; 2011. PMID: 21788544.
- Sittig DF, Singh H. Electronic Health Records and National Patient Safety Goals. New England Journal of Medicine 2012 Nov 8;367(19):1854-60. doi: 10.1056/NEJMsb1205420.
- Sittig DF, Singh H. Legal, Ethical, and Financial Dilemmas in Electronic Health Record Adoption and Use. Pediatrics 2011; 127:e1042–e1047. PMID: 21422090. doi:10.1542/peds.2010-2184.
- Sittig DF, Singh H. Rights and Responsibilities of Physician Users of Electronic Health Records. Canadian Medical Association Journal Feb 13, 2012. PMID: 22331971. doi:10.1503/cmaj.111599.
- Sittig DF, Wright A, Meltzer S, Simonaitis L, Evans RS, Nichol WP, Ash JS, Middleton B. Comparison of clinical knowledge management capabilities of commercially-available and leading internally-developed electronic health records. BMC Medical Informatics & Decision Making. 2011 Feb 17;11(1):13. PMID: 21329520.
- Wright A, Feblowitz J, McCoy AB, Sittig DF. Comparative Analysis of the VA/Kaiser and NLM CORE Problem Subsets: An Empirical Study Based on Problem Frequency. AMIA Fall Symposium, 2011:1532-40. PMID: 22195218.
- Wright A, Feblowitz J, Samal L, McCoy AB, Sittig DF. The medicare electronic health record incentive program: provider performance on core and menu measures. Health Serv Res. 2014 Feb;49(1 Pt 2):325-46. doi: 10.1111/1475-6773.12134.
- Wright A, Henkin S, Feblowitz J, McCoy AB, Bates DW, Sittig DF. Federal Incentives for Electronic Health Record Adoption: Early Results of the HITECH Act New England Journal of Medicine. 2013 Feb 21;368(8):779-80. doi: 10.1056/NEJMc1213481.
- Wright A, McCoy AB, Henkins S, Flaherty M, Sittig DF. Validation of an association rule mining-based method to infer associations between medications and problems. Appl Clin Inform. 2013 Mar 6;4(1):100-9. doi: 10.4338/ACI-2012-12-RA-0051.
- Wright A, McCoy AB, Henkin S, Kale A, Sitting DF. Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions. J Am Med Inform Assoc. 2013 Sep-Oct;20(5):887-90. doi: 10.1136/amiajnl-2012-001576.
- Wright A, Pang J, Feblowitz JC, Maloney FL, Wilcox AR, Ramelson HZ, Schneider LI, & Bates DW. A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record. J Am Med Inform Assoc 2011;18:6;859-867. doi:10.1136/amiajnl-2011-000121 PMID: 21613643.
- Xu H, Stenner SP, Doan S, Johnson KB, Waitman LR, Denny JC. MedEx: A medication information extraction system for clinical narratives. J Am Med Inform Assoc 2010; 17:1;19-24. doi: 10.1197/jamia.M3378